Technical Field
The present invention relates to prediction tools, and more particularly to predicting the associations between drug side-effects and therapeutic indications.
Description of the Related Art
Inferring potential therapeutic indications (e.g., drug repositioning), for either novel or approved drugs, has become a key approach in drug development. Recently, a number of computational methods have been developed to predict drug indications. There are four typical computational strategies in drug repositioning: (1) predicting drug indications on the basis of the chemical structure of the drug; (2) inferring drug indications from protein targets interaction networks; (3) identifying relationships between drugs based on the similarity of their phenotypic profiles; and (4) integrating multiple properties (e.g., chemical, biological, or phenotypic information) of drugs and diseases to predict drug indications.
These strategies focus primarily on using preclinical information using either chemical structures or protein targets, but clinical therapeutic effects are not always consistent with preclinical outcomes. Such chemical and biological information exhibits translational issues and is noisy when off-target binding occurs. Existing studies have build disease-side-effect associations based on all known drug-disease and drug-side-effect information, but such associations are very limited in number, and are biased from current observations.